Assessing water status in wheat under field conditions using laserinduced chlorophyll fluorescence and hyperspectral measurements
Bodo MisteleA, Salah ElsayedB, Urs SchmidhalterA
A
Department of Plant Sciences, Technische Universität München,
B
Branch of Agricultural Engineering, Evaluation of Natural Resources Department,
Environmental Studies and Research Institute, Minufiya University, Sadat
Abstract
Classical measurements for estimating water status in plants using oven drying
or pressure chambers are tedious and time-consuming. In the field, changes in
radiation conditions may further influence the measurements and thus require fast
measurements. The possibility to detect drought stress in wheat with laserinduced chlorophyll fluorescence and hyperspectral measurements at canopy level
under temperate field conditions was tested in this work. Three drought scenarios
were established by using a removable rainout shelter. A laser-induced
chlorophyll fluorescence sensor and a hyperspectral sensor were mounted on a
metal carrier about 2 m above the canopy. Our results show that the canopy water
content (%) was closely related to chlorophyll fluorescence at 690 nm, 730 nm
and to the biomass index for all cultivars (R2- values) with R2 ≥ 0.82***. A closer
relationship between spectral measurements and canopy water content and canopy
water mass was found than with leaf water potential. Selected spectral indices
were significantly related to canopy water content and canopy water mass (R2=
0.53*** – 0.96***). Two spectral indices and the fluorescence intensity at 690
nm were significantly related to the leaf water potential (R2= 0.58* – 0.83** and
R² ≥ 0.62). Our data show that non-destructive methods may enable to assess
water status information.
Keywords:
maize, phenotyping, reflectance, spectrometer, water status, wheat.
Introduction
Drought is the most important limiting factor for crop production and it is
becoming an increasingly severe problem in many regions of the. A rapid
assessment of the plant water status is useful for irrigation management purposes
and would also allow an efficient screening of large populations of plants as part
of a high-throughput system to precisely evaluate the phenotype for breeding
purposes (Schmidhalter, 2005a; Winterhalter et al., 2011a).
Whereas spectral relationships at various wavelengths have frequently been
described for parameters like whole plant water content (Liu et al. 2004), less
information is available regarding their ability to estimate the leaf water potential
(LWP). Measuring LWP is desirable because it directly reflects the effects of
drought, whereas plant (canopy) water content (CWC) can also be reduced by
many other stresses or factors that decrease biomass (e.g., nutrient deficiency, soil
aeration status, soil resistance or pathogens) and thus the CWC. Drought reduces
plant water content, but it may also be decreased during plant development due to
changes in the amount of structural tissues being formed. Given the potential
advantages of LWP as a more direct indicator of the leaf water status and its
attendant benefits for irrigation scheduling, simple, efficient methods to
characterize LWP are highly desirable.
The spectral reflectance of wheat leaves has been reported to be closely related
to changes in LWP under growth chamber conditions, with the best correlation of
LWP to reflectance being found for the normalized difference vegetation index
(NDVI; global R2 = 0.81), but also at wavelengths of 1450 nm (R2 = 0.92)
(Ruthenkolk et al. 2001). In addition, Gutierrez et al. (2010) found that the
normalized water index (R970 - R880)/ (R970 + R880) was significantly related to
LWP of wheat (R2 > 0.6-0.8) across a broad range of values (-20 to -40 bar).
Instead, proximal remote sensing systems depending on LICF show great
promise in detecting water stress and nitrogen levels (N) in crops (Apostol et al.,
2003; Zhang et al., 2005; Bredemeier and Schmidhalter, 2005; Thoren and
Schmidhalter, 2009) due to the inherent competition between chlorophyll
fluorescence and both photochemistry (PSI and PSII) and heat dissipation. Hence,
any change in the yield of these two processes will lead to a corresponding change
in the fluorescence yield (Lichtenthaler and Rinderle, 1988). This is true even in
light of the fact that the intensity of the fluorescence emission is <3% of that of
the absorbed light (Stober and Lichtenthaler, 1993); fluorescence emission
remains a standard method for detecting plant stress (e.g., water deficit,
temperature stress, nutrient deficiency, polluting agents, and attack by pathogens)
(Stober et al., 1992). Fluorescence has successfully been used to detect water
stress (Günther et al., 1994; Apostol et al., 2003; Bredemeier and Schmidhalter,
2005; Zhang et al., 2005) in plants. However, most of these studies were done at
the leaf level and under controlled conditions, thereby limiting their practical
application to field studies.
Surprisingly few studies have examined the relationship between water status
(water content and leaf water potential) and chlorophyll fluorescence, the
exceptions being Hsiao et al. (2004) and Schmuck et al. (1992). The former study,
albeit under controlled conditions, revealed that the water content and water
potential of vegetable plug seedlings were related to several measurements and
indices of chlorophyll fluorescence at 720 nm. The latter study echoes these
findings, indicating that both the variable chlorophyll fluorescence at 690 nm, and
730 nm are good indicators of the water content and water potential of maize.
However, spectral reflectance and LICF is also affected by many factors under
field conditions. It depends on complex interactions between several internal and
external factors. For instance, spectral reflectance is influenced not only by the
plant water status, but also by leaf thickness (Ourcival et al. 1999), differences in
leaf surface properties (Grant et al. 1993), soil background, and non-water stress
related variation in leaf angle, canopy structure (Asner 1998), leaf area (Sims and
Gamon 2003), canopy architecture, measuring angle, solar zenith and row spacing
(Jackson and Huete 1991).
The aims of this study were (i) to study the stability of newly developed
high throughput precision technique to detect water status in plants by measuring
the leaf water potential, which can reflect the water status of plants in a short time
scale, or the canopy water content and the canopy water mass which can reflect
the water status of plants in the longer run under temperate environmental
conditions; (ii) to estimate the most suitable spectral reflectance indices or LICF
parameter which can be used for high throughput precision phenotyping or
irrigation scheduling to detect the water status in wheat.
.
Materials and methods
Experimental setup
The experiments were conducted under field conditions at the research station
of the Chair of Plant Nutrition of the Technische Universität München in 2005
and 2008. Four cultivars of wheat (Ludwig, Ellvis, Empire, and Cubus) were
sown on October 6, 2004, at a seeding rate of 300 seeds per m2 and two cultivars
(Cubus and Mulan) were sown mid of October, 2007. Residual soil nitrate levels
of 42 kg NO3-N ha-1 were detected by a quick-test procedure (Schmidhalter,
2005b). Liquid urea ammonium nitrate fertiliser was split into two portions of 80
kg N ha-1 in BBCH 20 and 40 kg N ha-1 in BBCH 30. Phosphorus and potassium
supplies in the soil were adequate, requiring no further applications. The soil at
the research station is characterised as 60% silt, 25% clay, and 15% sand with pH
(CaCl2) 6.2. The water holding capacity of the soil is high (250 mm down to 1.2
m depth).
The experiment was a two-factorial setup with different winter wheat cultivars
(four in 2005 and two in 2007) and four water treatments (rainfed, irrigated
control, early water stress and late water stress). To control the amounts of water
applied to the plants, an automatic removable rain-out shelter platform was used
together with spray irrigation. The experiment included 28 plots, each 4 m long
and 1.8 m wide. Each regime was applied to two plots in 2005 and four plots in
2007 for each cultivar, except for the rainfed treatment, which had only a single
replicate. First, the plants were grown under rainfed conditions and then, early
stress (withholding water from begin of May to mid of June) and late water stress
(withholding water from end of May 28 to begin of July) were applied.
Sensor measurements
For fluorescence measurements, a fluorescence sensor developed by Planto
GmbH (Leipzig, Germany) connected to a portable computer (Thoren and
Schmidhalter, 2009) was used and mounted on a self-moveable metal carrier. The
sensor was mounted at a height of 3 m with a zenith angle of 45° above the plant
canopy. The sensor used a pulsed laser beam (λ 640 nm, 3 mm in diameter) to
induce chlorophyll fluorescence emission, which was measured at 690 nm (F690)
and 730 nm (F730) 2000 times per second and averaged to one single value per
second.
For spectral reflectance measurements, a passive reflectance sensor for
measuring at wavelengths between 300 - 1100 nm connected with a portable
computer and Geographical positioning system (GPS) was used. The sensor
consists of one optics and an automatic reference white plate to measure
subsequently canopy reflectance and sun reflectance in around 15 sec. The optics
was positioned at a height of 2 m above the plants in the nadir direction. The
aperture was 12° and the size of the field of view was 0.42 m2. With the readings
from the spectrometer unit the canopy reflectance was calculated and corrected
with a calibration factor estimated from a reference white standard.
In this study, we calculated and tested reflectance indices as (R780 - R670)/(R780 + R670),
(R410 - R780)/(R410 + R780), (R490 - R780)/(R490 + R780), (R510 - R780)/(R510 + R780) and R600/R780.
Sensor measurements were taken several times during the growing period from
stem elongation until ripening (Table 1). The different sensor values were related
to classically-determined values of CWC, canopy water mass (CWM) LWP.
To measure LWP, a pressure chamber (PMS Instrument, Corvallis, OR, USA)
was used (Schmidhalter et al., 1998). Pressures were read within one minute of
leaf removal from the plant and LWP was determined as the average value from
six separate readings of fully-expanded flag leaves.
To determine aboveground aerial biomass, plants were cut above the ground
within a 0.22 m2 area (A). A representative subsample was then dried in an oven
(105°C) until there was no change in dry weight (DW). Canopy water content (in
Table 1. Sensor measurements and physiological parameters at different
growth stages, dates and times.
Growth stage
Date
Sensor
mesurements
Time of day
Stem elongation
Stem elongation and
inflorescence
emergency
Inflorescence
emergence and anthesis
Anthesis and milk
development
May 25, 2005
12:45 – 13:19
June 1, 2005
12:46 – 13:40
June 8, 2005
14:11 – 15:04
June 21, 2005
13:41 – 14:14
Canopy water content, canopy
water mass
Leaf water potential, canopy water
content, canopy water mass
Anthesis and milk
development
Milk development and
ripening
June 23, 2005
13:40 – 14:15
Leaf water potential
July 4, 2005
10:42 – 11:10
Leaf water potential, canopy water
content, canopy water mass
Heading
June 2, 2008
13:38 - 14:30
Leaf water potential,
canopy water content,
and canopy water mass
Heading and flowering
June 10, 2008
13:25 - 14:20
Leaf water potential
Milk and dough
July 2, 2008
14:56 - 15:50
Leaf water potential,
canopy water content,
and canopy water mass
Physiological parameters
measured
Leaf water potential
Leaf water potential
%) was calculated as CWC = (FW – DW)/FW * 100. In addition, canopy water
mass (in g/m2) was calculated as CWM = (FW – DW)/area.
Results
The relationship between canopy water content and spectral indices of two
wheat cultivars subjected to four watering regimes
The coefficients of determination for the relationships between five spectral
indices and canopy water content of Cubus and Mulan at the individual
measurements and combining data for each cultivar are given in Table 2. As well
as, the relationships between four indices and canopy water content of Cubus at
the individual measurements and the combined data for each cultivar are shown in
Figure 1. The results demonstrated that all spectral indices were significantly
related with canopy water content of Cubus and Mulan at individual
measurements and across all measurements for two harvest times, except one
measurement day for Mulan at June 2 (R2 ≥ 0.75; p ≤0.001). Positive relationships
between spectral indices and canopy water content of two cultivars, except
R600/R780 were found. The highest coefficient of determination could be shown
between (R510 - R780)/(R510 + R780) and canopy water content of Cubus (R2 = 0.96;
p ≤ 0.001 ) with the combined data in Figure 1.
(R410-R780)/(R410+R780)
June 2, 08 R2 = 0.92***
July 2, 08 R2 = 0.81***
Combind R2 = 0.96***
-0.70
-0.75
-0.80
-0.85
-0.90
-0.95
1.00
(R490-R780)/(R490+R780)
(R510-R780)/(R510+R780)
Cubus
-0.65
NDVI
0.90
0.80
0.70
R2 = 0.87***
R2 = 0.75***
R2 = 0.92***
0.60
0.50
0.40
50
55
60
65
70
75
80
85
-0.80
-0.82
R2 = 0.91***
R2 = 0.91**
R2 = 0.85***
-0.84
-0.86
-0.88
-0.90
-0.92
-0.94
-0.65
R2 = 0.92***
R2 = 0.85***
R2 = 0.95***
-0.70
-0.75
-0.80
-0.85
-0.90
-0.95
50
55
60
65
70
75
80
Canopy water content (%)
Figure 1: The relationship between canopy water content and four spectral
indices for Cubus subjected to four watering regimes. Data were pooled across
four watering regimes. Measurements were taken at two dates and the regressions
over all were fitted.
85
Table 2: Coefficients of determination of the relationship between canopy
water content and spectral indices for two winter wheat cultivars subjected to four
watering regimes at two dates.
Cultivas
Dates
NDVI
(R510-R780)/
(R510+R780)
(R490-R780)/
(R490+R780)
(R410-R780)/
(R410+R780)
R600/R780
Cubus
June 2, 08
July 2, 08
0.87***
0.75***
0.92***
0.81***
0.92***
0.85***
0.91***
0.91***
0.87***
0.76***
combined data
0.92***
0.96***
0.95***
0.85***
0.93***
Mulan
June 2, 08
July 2, 08
combined data
0.11
0.83***
0.94***
0.05
0.82***
0.93***
0.08
0.83***
091***
0.01
0.84***
0.50***
0.02
0.81***
0.94***
All cultivars
combined data
0.93***
0.89***
0.89***
0.82***
0.92***
*, **, *** Statistically significant at P ≤ 0.05; P ≤ 0.01 and P ≤ 0.001, respectively
The relationship between canopy water mass and spectral indices of wheat
cultivars subjected to four watering regimes
(R410-R780)/(R410+R780)
(R510-R780)/(R510+R780)
Cubus
-0.60
June 2, 08 R2 = 0.83***
July 2, 08 R2 = 0.80***
Combind R2 = 0.78***
-0.65
-0.70
-0.75
-0.80
-0.85
-0.90
-0.95
(R490-R780)/(R490+R780)
1.00
0.90
NDVI
0.80
0.70
R2 = 0.86***
R2 = 0.74***
R2 = 0.82***
0.60
0.50
0.40
1000
2000
3000
4000
5000
6000
-0.80
-0.82
R2 = 0.85***
R2 = 0.81**
R2 = 0.83***
-0.84
-0.86
-0.88
-0.90
-0.92
-0.94
-0.65
R2 = 0.84***
R2 = 0.80***
R2 = 0.84***
-0.70
-0.75
-0.80
-0.85
-0.90
-0.95
1000
Canopy water mass
2000
3000
4000
5000
6000
(g/m2)
Figure 2: The relationship between canopy water mass and four spectral
indices for Cubus subjected to four watering regimes. Data were pooled across
four watering regimes. Measurements were taken at two dates and the regressions
over all were fitted.
Table 3: Coefficients of determination of the relationship between canopy
water mass and spectral indices for two winter wheat cultivars subjected to four
watering regimes at two dates.
Cultivars
Dates
NDVI
(R510-R780)/
(R510+R780)
(R490-R780)/
(R490+R780)
(R410-R780)/
(R410+R780)
R600/R780
Cubus
June 2, 08
July 2, 08
combined data
0.86***
0.74***
0.82***
0.83***
0.80***
0.78***
0.84***
0.80***
0.84***
0.85***
0.81***
0.83***
0.76***
0.75***
0.80***
June 2, 08
July 2, 08
combined data
0.14
0.85***
0.74***
0.07
0.86***
0.76***
0.11
0.87***
0.81***
0.01
0.88***
0.53***
0.01
0.85***
0.73***
combined data
0.78***
0.78***
0.79***
0.79***
0.76***
Mulan
All cultivars
*, **, *** Statistically significant at P ≤ 0.05; P ≤ 0.01 and P ≤ 0.001, respectively
Close relationships between all spectral indices and canopy water mass of
Cubus and Mulan were found at the individual measurements and across all
measurements for two harvest dates, except one measurement day for Mulan at
June 2 (R2 ≥ 0.53; P ≤ 0.001) shown in Figure 2 and Table 3. The highest
coefficient of determination was recorded between (R410 - R780)/(R410 + R780) and
canopy water mass for individual measurements of Mulan at July 2 (R2 = 0.88; P
≤ 0.001).
The relationship between leaf water potential and spectral indices of two
wheat cultivars subjected to four watering regimes
Leaf water potential showed good relationships with mainly two spectral
indices (R410 - R780)/(R410 + R780) and (R490 - R780)/(R490 + R780) (Fig. 3). However,
the relationships were affected by the date (ambient temperature and radiation
condition) and it was not possible to fit one single regression curve across all
measurements. Significant relationships between leaf water potential and two
spectral indices for Cubus and Mulan varied between R2 = 0.58* to 83**. The two
spectral indices were negatively related to the leaf water potential.
(R490-R780)/(R490+R780)
(a) Cubus
June 2 R2 = 0.66*
June 10 R2 = 0.83**
-0.75
July 2
R2 = 0.43
-0.80
-0.85
-0.90
-0.95
-0.82
(R410-R780)/(R410+R780)
(R490-R780)/(R490+R780)
(R410-R780)/(R410+R780)
-0.70
(b) Cubus
R2 = 0.59*
R2 = 0.81**
-0.84
R2 = 0.58*
-0.86
-0.88
-0.90
-0.92
-0.94
-24
-22
-20
-18
-16
-14
-0.74
(c) Mulan
R2 = 0.11
R2 = 0.74**
-0.76
-0.78
R2 = 0.60*
-0.80
-0.82
-0.84
-0.86
-0.88
-0.90
-0.92
-0.84
(d) Mulan
R2 = 0.01
R2 = 0.77**
-0.86
R2 = 0.66*
-0.88
-0.90
-0.92
-0.94
-20
-19
-18
-17
-16
-15
-14
-13
Leaf water potential (bar)
Figure 3: The relationship between leaf water potential and two spectral
indices of (a & b) Cubus and (c & d) Mulan subjected to four watering regimes.
The relationship between canopy water content and each of the
fluorescence intensity at 690 and 730 nm
The relationship between CWC and several fluorescence parameters were
determined across three biomass harvests from BBCH 57 to 90 (Fig. 4). The
fluorescence intensity at 690 nm and 730 nm increased with increasing CWC. The
fluorescence intensity at 690 and 730 nm behaved differently among all the
cultivars, although three of them (Empire, Ellvis, and Cubus) behaved similarly.
-12
Figure 4: The relationship between canopy water content and fluorescence
intensity at 690 and 730 nm. Data were pooled across four watering regimes and
all measurements dates and presented for each individual cultivar (left) and for all
cultivars together (right).
The relationship between canopy water mass and each of fluorescence
intensity at 690 and 730 nm
The relationship between CWM and several fluorescence parameter were
determined across three biomass harvests from BBCH 57 to 90 (Fig. 5).CWM
shows a linear relationship with all fluorescence parameters (when significant)
(Fig.5). Except Empire, the results largely mirrored those for CWC. Significant
positive relationships were found between CWM and chlorophyll fluorescence at
each of 690 nm and 730 nm, both for each individual cultivar and across all
cultivars pooled together.
Figure 5: The relationship between canopy water mass and fluorescence
intensity at 690 and 730 nm. Data were pooled across four watering regimes and
all measurements dates and presented for each individual cultivar (left) and for all
cultivars together (right).
The relationships between leaf water potential (bar) and each of
fluorescence intensity at 690 and 730 nm
Linear relationships between leaf water potential with fluorescence parameters
are shown in Fig. 6. Slightly lower values were observed for the relationship
between leaf water potential (bar) and fluorescence intensity at 690 nm for each
cultivar.
Figure 6: The relationship between leaf water potential and fluorescence
intensity at 690 and 730 nm. Data were pooled across four watering regimes and
all measurements dates and presented for each individual cultivar.
Discussion and conclusions
In this study high-throughput sensors were examined to access short term
drought stress and long term drought stress (CWC, CWP). LWP is the best short
term water stress predictor. Under mild climate conditions the plants become fully
rehydrated overnight and so the plants start without stress every morning. In this
experiment sensor values correlated better with CWC than with LWP. These
results are in contrast to Elsayed et al. (2011), where the spectral measurements
correlated best with LWP. But in this latter study only light conditions were
changed, all the other environmental effects were eliminated. This demonstrates
that sensors may be well suited to detect short term drought stress under such
conditions, but this may be more difficult to achieve under field conditions with a
single measurement because plants with lower LWP under field conditions show
usually also some long term drought stress effect like changes in biomass, leaf
structure or in canopy architecture. Influence on sensor values described for leaf
thickness (Ourcival et al. 1999), differences in leaf surface properties (Grant et al.
1993), soil background and non-water stress related variation in leaf angle,
canopy structure (Asner 1998), leaf area (Sims and Gamon 2003), canopy
architecture, measuring angle, solar zenith and row spacing (Jackson and Huete
1991). Such effects may have influenced the measurements in these experiments
and resulted in best correlations to CWC for all sensors.
Our results show the closest relationships of the fluorescence intensity at 690
and 730 nm to CWC. Hsiao et al. (2004) showed that the water content of
Brassica oleracea seedlings was related to Fm/Fs (R2 = 0.91) and RFd (R2 = 0.98)
with a fluorescence image system at 720 nm under controlled conditions (where
Fm, Fs, and RFd are the maximum, steady state, and variable chlorophyll
fluorescences, respectively). In contrast, Schmuck et al. (1992) found that the
variable chlorophyll fluorescence RFd in the 690 nm and 730 nm regions, used to
detect changes in water content, depended on species. In wheat plants, the water
stress treatment had no influence on the RFd values in either wavelength region,
whereas a clear trend was observed in maize plants.
The fluorescence intensity decreased with increasing canopy water content and
canopy water mass, as well as with decreases in leaf water potential; these results
agree with findings by Theisen (1988), who reported that when drought stress
became visible, strongly reduced fluorescence intensities at 685 nm and 730 nm
were observed. Günther et al. (1994) found that the fluorescence intensities at 685
nm and 730 nm of stressed oak tree branches (Quercus pubescens) were strongly
reduced in comparison to those of the healthy branches. In addition, Apostol et al.
(2003) found that the emission of the fluorescence intensity of plants with 25%
and 50% leaf water deficit was lower than for irrigated plants. In contrast,
Lichtenthaler and Rinderle (1988) found that the fluorescence intensity increased
with increasing water stress.
The inverse relationship between the stress level and the fluorescence index
points to reduced photosynthesis of photosystem II because of closed stomata.
Hence, the non-photochemical quenching is increased to prevent PS II
photooxidation (Baker, 2008), and this leads to an increase of the yield of heating
and decreases the fluorescence intensity by dissipating energy as heat. The
competition between chlorophyll fluorescence and both photochemistry (PS I and
PS II) and heat dissipation will lead to a corresponding change in the fluorescence
yield.
Our assessment of reflectance indices as a method to measure the water status
in wheat demonstrated that the selected five indices such as (R410 - R780)/(R410 +
R780), (R510 - R780)/(R510 + R780), (R490 - R780)/(R490 + R780), NDVI, and R600/R780
are apparently useful for describing water status of wheat canopies by using
canopy water content and canopy water mass regardless of growth stage at
individual measurements and across all measurements. This shows, that indices
are not only useful for irrigation scheduling when data are combined across all
measurements in one year, but might also be useful for evaluating the phenotype
for breeding purposes with individual measurements.
Some studies such as Liu et al. (2004) found that there were no relationships
between WI, NDWI, red edge position, and red edge position with plant water
content of wheat for most of the individual measurements. Our results are in an
agreement with Graeff and Claupein (2007), who found that the wavelength
ranges 510 - 780 nm (R2 = 0.79***), 540 - 780 nm (R2 = 0.79***), are most
suitable to describe the water content in wheat. The increase in reflectance at
these spectral regions may be attributed to a compound effect of a change in the
internal leaf structure and to a change in light absorption by photosynthetic
pigments due to altered photosynthetic activity. In additional, Winterhalter et al.
(2011b) found that the index R850/R725 was significantly related to canopy water
mass of maize (0.72***).
The index NDVI is associated with the leaf area index (LAI) according to
Aparicio et al. (2002) and with both chlorophyll content and LAI according to
Eitel et al. (2008). In this experiment, NDVI is strongly related to canopy water
content and canopy water mass at individual measurements and across all
measurements for each cultivar and all cultivars. But the relation may depend on
the LAI also.
The leaf water potential undergoes rapid temporal fluctuation as a function of
environmental conditions (Jensen et al., 1990). The results show that changes in
leaf water potential can reliably be detected by using spectral measurements under
field condition. LWP was related to two spectral indices (R410 - R780)/(R410 + R780),
and (R490 - R780)/(R490 + R780) at most of the individual measurements. Global
spectral relationships measuring LWP probably cannot be established across plant
development. Even so, spectrometric measurements supplemented by a reduced
calibration data set from pressure chamber measurements might still prove to be a
fast and accurate method for screening large numbers of cultivars
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